The development of financial econometrics as a field of research has
been
shaped by the availability of high-frequency data on any traded
asset price
over the past one and a half decades. Due to this development in
data
availability, a considerable amount of progress has been made in
the
estimation of realized variance as a measure of financial volatility
as well as
in the area of providing accurate forecasts of volatility based on
these
measures. To a much lesser extent, the literature has been
asking which economic forces are at play in the dynamics of
realized
variance, how realized (co)variances interact across different
markets and
asset classes, how the parameters of the proposed models evolve over
time,
and what implications realized variance has for asset and
derivatives
pricing. The project "Analysis and models of cross asset
dependency
structures in high-frequency data" contributes to the
literature on
realized variance by filling these gaps.

For practical purposes, we structure the project into two
subprojects. In
subproject one, we explore adaptive modeling techniques for
realized
variance measures at the univariate and the multivariate level.
Emphasis is
on parameter flexibility specified within a time-varying Bayesian
state-space model representation. This allows us to capture
alternating lag
and weight structures and also macroeconomic risk factors that drive
the
evolution of the economy. The second project focuses on the question
whether realized variance measures obtained from derivatives and
underlying markets can be reconciled under the assumptions of
standard option pricing models. Moreover, what assumptions are
needed in these models and how do intra-day jumps in the underlying
process propagate to the derivatives market. Lastly, we seek to
combine the insights gained from the two subprojects by developing
sophisticated risk-management tools and flexible option pricing
models based on information embodied in high-frequency data and the
flexible
models fitted to it.